Predict Cell Types, Probabilities, or Confidence Scores

Predicts cell type labels or class probabilities using a trained RankMap model. Supports both glmnet and cv.glmnet. Optionally returns a data frame with predicted cell types and confidence scores.

Usage

predictRankModel(
  model,
  new_data,
  lambda = NULL,
  return_probs = FALSE,
  return_confidence = FALSE,
  ...
)

Arguments

model

A fitted model from trainRankModel.

new_data

A gene expression matrix with genes as rows and cells (or spatial spots) as columns. Can be a Seurat object, a SummarizedExperiment object, a dense numeric matrix, or a sparse dgCMatrix.

lambda

Optional lambda value. If NULL, uses lambda.min (if available), else 0.01.

return_probs

Logical. If TRUE, returns the full matrix of class probabilities.

return_confidence

Logical. If TRUE, returns a data frame with predicted labels and confidence scores.

...

Additional arguments passed to computeRankedMatrix.

Value

A character vector (default), a matrix (if return_probs = TRUE), or a data frame (if return_confidence = TRUE).

Examples

# Read in single-cell reference data
seu_sc <- readRDS(system.file("extdata", "seu_sc.rds",
    package = "RankMap"
))

# Read in Xenium spatial data
seu_xen <- readRDS(system.file("extdata", "seu_xen.rds",
    package = "RankMap"
))

# Extract normalized expression data
common_genes <- intersect(rownames(seu_sc), rownames(seu_xen))
mat <- extractData(seu_sc)[common_genes, ]
new_mat <- extractData(seu_xen)[common_genes, ]

# Train a model
set.seed(42)
model <- trainRankModel(mat, seu_sc$cell_type)

# Predict cell type
pred <- predictRankModel(model, new_mat)

table(predict = pred, truth = seu_xen$cell_type_SingleR)
#>        truth
#> predict Basal LP Tumor
#>   Basal    48  4     1
#>   LP        2 45     0
#>   Tumor     0  1    49